ISYE 412 teaches the fundamentals of using data analytics to make data-driven decisions, emphasizing the application of modeling strategies and analytics principles to industrial engineering problems.
Weekly homework consists of four to five questions covering lecture material, with an optional three percent extra credit opportunity available.
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(I SY E 210,E C E 331,STAT 311, 324,MATH/STAT 309, 431, or MATH 531), graduate/professional standing, or member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Tina Xu reads from dense slides and provides illegible handwritten examples, which reviewers find unhelpful and boring. Some students report she is irritated by questions and fails to clarify concepts, leading to poor course quality ratings.
Other students describe Xu as sweet and likeable, noting that her in-class examples and clear grading make the course an easy A with regular attendance and homework completion. These reviewers appreciate her extra credit and straightforward exam outlines.
Recorded history may be incomplete and does not establish a future schedule.
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| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 52 / 54 | 0 |
| LAB 301 | Classroom Instruction | 26 / 27 | 0 |
| LAB 302 | Classroom Instruction | 26 / 27 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Tina Xu offers a straightforward path to high grades through clear exams and manageable homework, though lectures are often described as dull.
Recent recorded grades — Spring 2025: 3.71 GPA, 82.1% A/AB (n=78 letter grades); Fall 2025: 3.50 GPA, 71.4% A/AB (n=42 letter grades); Spring 2026: 3.58 GPA, 72.9% A/AB (n=59 letter grades).
Weekly homework consists of four to five questions covering lecture material, with an optional three percent extra credit opportunity available.
Students report that slides are text-heavy and lectures are dry, while handwritten examples are often illegible and difficult to follow.
– average GPA
Approximate 80% prediction interval
The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.
The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.
6 earlier forecasts · 0.14 GPA average error.
Through Fall 2026
Latest available grades · Spring 2026 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Descriptions, prerequisites, and recorded course offerings.
Observations at scan time; dates do not imply when a catalog change took effect.
Fundamentals of Industrial Data Analytics
Recorded 2026-09-07[
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"observed_at": "2026-09-07 15:55:43.033547+00:00",
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"course_id": "ISYE 412",
"course_uid": "course_4e8831b837f359daa4b2cadc",
"term_id": "1272",
"source_course_id": "024587",
"source_subject_id": "490",
"title": "Fundamentals of Industrial Data Analytics",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Fall, Spring"
}
]Original comments behind the course and instructor summaries.
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded model configuration, reasoning, and tool conversations.
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}